A practical framework for enterprise teams deciding when to use agentic AI versus traditional automation approaches like workflows, RPA, and copilots. The core argument is that decision complexity — not task volume or novelty — is the key signal for choosing agents. Deterministic automation remains superior when rules are clear and inputs are structured. Agents earn their place when tasks involve ambiguous inputs, multi-step reasoning, and frequent exceptions. The post provides a decision checklist, a hybrid architecture pattern (agent + rules engine + workflow + human review), and best practices including narrow tool access, structured output validation, escalation paths, and blast-radius thinking. Governance is framed as an architectural concern, not an afterthought.
